It is known that EGM signals can be collected by use of electrodes placed on endocardial or epicardial leads that are implanted with the device. These signals, directly correlated to the electrical activity of cardiac cells, provide much useful information for the purpose of assessing the patient's condition. Hence, after amplifying, digitizing and filtering, they are mainly utilized to control the cardiac pacer and diagnose some rhythm disorders requiring, for example, automatic triggering of an antitachycardia, antibradicardia, or interventricular resynchronization therapy, through implementing advanced analysis and decision taking algorithms.
However, when it comes to analyzing the heart rhythm in a subjective way, in order to perform a diagnostic or readjust the parameters of an implanted device, the order to perform a diagnostic or readjust the parameters of an implanted device, the practitioners prefer, in practice, to interpret the information given by the surface electrocardiogram (ECG). An ECG allows one to visualize in a direct manner, a certain number of determining factors (QRS width, etc.) and thereby weigh the evolution of a heart failure.
Indeed, the ECG and EGM signals, though they actually have the same source (the electrical activity of myocardium), visually appear in much different manners: the EGM collected by the implantable device provides local information on the electrical activity of a group of heart cells, whereas the ECG appears in the form of more global information, influenced by the propagation of the electrical signal between the myocardium and body surface, and by a certain number of morphologic and pathologic specificities. Thus, the display of EGM signals is not very useful to a practitioner who is used to interpreting surface ECG signals.
It is also usually the ECG signals that are recorded over a long period of time through ambulatory practice by Holter recorders, so as to be further processed and analyzed in order to evaluate the clinical condition of the patient and eventually diagnose whether a heart rhythm disorder is present.
Hence, when a patient implanted with a medical device comes to his practitioner for a routine visit, the practitioner uses two distinct devices: an ECG recorder and an external implant programmer. In order to collect the ECG signal, the practitioner places a certain number of electrodes on the patient's torso, so as to define the usual twelve useful leads corresponding to as many distinct ECG signals. As to the external programmer, it is used to control certain operating parameters of the implantable device (for example, the battery life), download data from the implantable device memory, and eventually to modify the parameters thereof, or upload an updated version of the device operating software, etc.
The visit with the practitioners therefore usually requires two different devices, as well as specific manipulations for placing the surface electrodes and collecting the ECG signals.
Moreover, the use of these two devices requires the patient to come to a specifically equipped center, usually having the consequence of routine visits that are spaced farther apart, resulting in a less rigorous follow-up of the patient.
In order to overcome such drawbacks, some algorithms for reconstructing a surface ECG based upon EGM signals (that is from the signals directly provided by the implantable device) have been developed. Indeed, the reconstruction of the surface ECG based upon EGM signals would allow:                to avoid, during routine visits, having to place surface electrodes and resort to an ECG recorder;        to therefore render the visit simpler and quicker, eventually allow performing the routine visit at the patient's home, and subsequently shorten the intervals between successive visits, and improve the patient's follow-up; and to eventually allow a remote transmission of the EGM data recorded by the implanted device, without the intervention of a practitioner or medical aid.        
Various algorithms for surface ECG reconstruction based upon EGM signals have been proposed so far. U.S. Pat. No. 5,740,811 (Hedberg, et al.) proposes to synthesize a surface ECG signal by combining a plurality of EGM signals by means of a neural network and/or fuzzy logic and/or summer circuit, after learning performed by an algorithm of the “feedforward” type. Such technique, operating through a linear unidimensional filtering, has the drawback of producing an output signal that is corresponding to only one lead of the surface ECG, and therefore only provides the practitioner with a very narrow vision of the patient's cardiac activity, compared to the usual twelve leads provided by an external ECG recorder. Another drawback of such technique is that it does not take into account the position of the endocardial leads, which may change between the moment of the learning process and that of the use of the device, a change in the heart electrical axis will have the effect of biasing the synthesized ECG signal, which will no longer be meaningful, with a risk to mask the heart disorder which may then not be diagnosed.
U.S. Pat. No. 6,980,850 (Kroll, et al.) proposes to overcome this difficulty, by proposing a method of surface ECG reconstruction implementing a matrix transform allowing to render each of the surface ECG leads individually. Such transform also allows to take into account several parameters, such as patient's respiratory activity or posture, which influence tracking the position of the endocardial leads through space. The proposed reconstruction consists of transforming, through a predetermined transfer matrix, an input vector representative of a plurality of EGM signals into a resulting vector representative of the different ECG leads. The transfer matrix is learned through averaging plural instant matrices based upon ECG and EGM vectors recorded simultaneously over a same period of time along a learning phase.
Although this technique brings an improvement to that proposed in the previous cited patent, it nevertheless presents certain drawbacks. First, it makes the assumption there exists a linear relationship between ECG and EGM vectors: such an approximation, though relatively accurate with patients presenting a regular rhythm, leads in some cases to important errors of ECG reconstruction in the presence of atypical or irregular signal morphologies—corresponding precisely to potentially pathologic cases. Moreover, the parameters of the transfer matrix are determined during a learning phase corresponding to a patient condition at a given moment. Such a situation may no longer be representative several weeks or months later, notably due to the evolution of the patient's pathology; such evolution will not be taken into account by the algorithm, except if the patient is requested to come again to a clinical center for a recalibration of the algorithm (calculation of a new transfer matrix).
Other approaches have also been proposed, such as that described in US patent application US 2005/0288600 (Zhang et al.), which consists of using, instead of EGM signals (which require the use of electrodes placed on endocardial electrodes), some subcutaneous ECG signals collected by means of a reduced number of electrodes directly placed on the surface of the implanted device's case. The ECG is then directly obtained from the inside of the patient's body instead of being obtained from surface electrodes applied on the skin, as with standard ECG recorders. The collected different subcutaneous ECG signals are split and undergo an analysis (morphology, time intervals, frequential analysis) as a function of criteria stored in a memory. The result of this analysis is compared to a reference that has been previously memorized and updated by the system, notably when some changes occur. The analysis of the signals allows to follow the evolution of the patient's heart rhythm so as to perform a cardiac diagnostic.
However, making electrodes on the surface of a case is not easy to do from a technological point of view.